Tropical Geography ›› 2020, Vol. 40 ›› Issue (2): 229-242.

### Analysis of Spatio-Temporal PM2.5 Patterns Obtained Using Mobile Monitoring: Case Study Conducted in Central District of Guangzhou

Song Jie1, Zhou Suhong1(), Peng Yinong2, Lin Rongping1, Xu Jianbin1a

1. 1.a. School of Geography and Planning, Sun Yat-Sen University; b. Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China
2.Guangzhou Urban Planning and Design Survey Research Institute, Guangzhou 510060, China
• Received:2019-09-24 Revised:2020-01-07 Online:2020-03-10 Published:2020-05-15
• Contact: Zhou Suhong E-mail:eeszsh@mail.sysu.edu.cn

Abstract:

It is crucial to address the global risk of disease caused by PM2.5 air pollution, which requires large-scale monitoring of PM2.5 pollution. Simulations of pollutant patterns are also necessary; however, it is currently difficult to accurately depict the spatial and temporal distribution patterns of PM2.5 pollution in cities using traditional air pollutant simulation methods. In this study, basic low-cost air quality monitoring equipment was used to conduct mobile monitoring of PM2.5 pollution in the central urban area of Guangzhou within the ring expressway, and 2,257,000 PM2.5 monitoring data were obtained at a frequency of 1 Hz. Using these data, simulation of PM2.5 pollution within the study area was conducted at a spatial and temporal resolution of 10 m × 10 m, and the reliability of collecting spatial and temporal patterns of PM2.5 pollution via the mobile device in the urban center was analyzed. The mobile monitoring data results showed the following: under stable weather conditions, there was a significant temporal correlation between PM2.5 data obtained under mobile monitoring and that from the fixed monitoring station (R 2: 0.72-0.86). The spatial and temporal distributions of PM2.5 pollution in the central area of Guangzhou showed significant spatial and temporal differentiations over short time periods. Temporally, the hourly average ranges in dry and wet seasons were 27 μg/m3 and 11 μg/m3, respectively, where the temporal periods of the highest and lowest concentrations occur depends were related to the background concentrations on the day. Spatially, there were higher values of PM2.5 near transportation hubs, commercial centers, industrial parks, and large commercial markets; however, lower values were found in parks, green areas, and high-end residential areas and on university campuses. Furthermore, spatial differentiation characteristics were evident, with values higher in the west and south and lower in the east and north during the dry season, but higher in the east and lower in the west during the wet season. Although there was no temporal correlation between high PM2.5 values during the day and peak traffic periods, pollution was spatially concentrated in the vicinity of important traffic nodes within the city, and the amount increased during peak traffic periods. These results show that the mobile monitoring method can be used to describe the spatial and temporal patterns of pollutants and key areas of exposure can be identified, which is of great significance for optimizing and adjusting the layout structure of monitoring sites and associated maintenance costs. Implementation of this method could enable the identification of high risk pollution routes, which would prevent and control pollution, improve the ecological environment, and enable targeted protection measures to be effectively evaluated. As such, use of mobile monitoring is important in the construction of smart cities and for realizing the long-term and high-precision air quality monitoring within cities under the support of smart geospatial technology.

CLC Number:

• X513